Predicting Genetic Markers for Brain Tumors Using a Composite Loss

Brain cancer has a very high mortality rate. Gliomas are the most common malignant brain tumors, causing this severe fatality. Recent biological investigations revealed that a holistic study of biomarkers, responsible for causing genetic mutations in gliomas, can ensure a comprehensive prognosis and treatment plan for the patients. In this paper, we simultaneously predict five such important genetic markers, namely, IDH, 1p/19q codeletion status, ATRX, MGMT, and TERT from Whole Slide Images using deep learning. At the heart of our deep learning based solution lies a novel composite loss function, which uniquely combines individual, pairwise and groupwise traits of the above bio-markers. While multi-label weighted cross-entropy loss captures individual characteristics, a conditional probability loss was developed for pairwise behavior, and, a spectral graph loss was formulated for modeling group properties. Comprehensive experimentation, along with an ablation study, clearly demonstrates the effectiveness of our solution, by achieving state-of-the-art prediction.